Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...
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Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced ***, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
DO we need a fundamental change in our professional culture and knowledge foundation for control and automation?If so,what are necessary and critical steps we must take to ensure such a change would take place effecti...
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DO we need a fundamental change in our professional culture and knowledge foundation for control and automation?If so,what are necessary and critical steps we must take to ensure such a change would take place effectively and efficiently,or more general,smoothly and sustainably?
Dear Editor,Modeling is the first and essential step for control and automation,and large models,from current Chat GPT or large language models(LLMs)to future large knowledge models of knowledge automation,would be th...
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Dear Editor,Modeling is the first and essential step for control and automation,and large models,from current Chat GPT or large language models(LLMs)to future large knowledge models of knowledge automation,would be the foundation model and infrastructure intelligence for coming intelligent industries and smart societies.
We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remot...
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We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the developmen...
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].
COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief...
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COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.
Dear Editor,Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simpl...
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Dear Editor,Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired.
Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change *** detection is fundamental to many computer vision *** existing solutions based on ...
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Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change *** detection is fundamental to many computer vision *** existing solutions based on deep neural networks are able to achieve impressive results.
It was a shock and disbelief to learn of Peter's death last *** me,he was so fit in his figure,so healthy in his lifestyle,so mild and mindful in his social behavior,and so devout in his religious belief,......,I ...
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It was a shock and disbelief to learn of Peter's death last *** me,he was so fit in his figure,so healthy in his lifestyle,so mild and mindful in his social behavior,and so devout in his religious belief,......,I had expected a long and happy life for him,and planned to join his 80th or even 100th birthday celebration.
Hand gesture recognition has become a vital subject in the fields of human-computer interaction and rehabilitation *** paper presents a multi-modal fusion for hand gesture recognition(MFHG)model,which uses two heterog...
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Hand gesture recognition has become a vital subject in the fields of human-computer interaction and rehabilitation *** paper presents a multi-modal fusion for hand gesture recognition(MFHG)model,which uses two heterogeneous networks to extract and fuse the features of the vision-based motion signals and the surface electromyography(s EMG)signals,*** extract the features of the vision-based motion signals,a graph neural network,named the cumulation graph attention(CGAT)model,is first proposed to characterize the prior knowledge of motion coupling between finger *** CGAT model uses the cumulation mechanism to combine the early and late extracted features to improve motion-based hand gesture *** the s EMG signals,a time-frequency convolutional neural network model,named TF-CNN,is proposed to extract both the signals'time-domain and frequency-domain *** improve the performance of hand gesture recognition,the deep features from multiple modes are merged with an average layer,and then the regularization items containing center loss and the mutual information loss are employed to enhance the robustness of this multi-modal ***,a data set containing the multi-modal signals from seven subjects on different days is built to verify the performance of the multi-modal *** experimental results indicate that the MFHG can reach 99.96%and 92.46%accuracy on hand gesture recognition in the cases of within-session and cross-day,respectively.
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